# Single-shot Embedding Dimension Search in Recommender System

arXiv: https://arxiv.org/abs/2204.03281 · HTML: https://mlsanity.com/paper/2204.03281/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2022.

## Built on by (most cited first, 13 of 13)

- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Experimental Analysis of Large-scale Learnable Vector Storage Compression](https://mlsanity.com/paper/2311.15578/) (2023)
- [Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation](https://mlsanity.com/paper/2402.02855/) (2024)
- [A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems](https://mlsanity.com/paper/2406.17335/) (2024)
- [Boosting Factorization Machines via Saliency-Guided Mixup](https://mlsanity.com/paper/2206.08661/) (2022)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective](https://mlsanity.com/paper/2411.13052/) (2024)
- [Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems](https://mlsanity.com/paper/2401.04408/) (2024)
- [Unified Low-rank Compression Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2405.18146/) (2024)
- [AdaS&S: a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System](https://mlsanity.com/paper/2411.07504/) (2024)
- [ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning](https://mlsanity.com/paper/2503.09315/) (2025)
- [DimGrow: Memory-Efficient Field-level Embedding Dimension Search](https://mlsanity.com/paper/2505.12683/) (2025)

## Abstract (arXiv)

As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR prediction tasks, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model.